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精子实时自动追踪与捕获系统

Real-time automated tracking and trapping system for sperm.

作者信息

Shi Linda Z, Nascimento Jaclyn, Chandsawangbhuwana Charlie, Berns Michael W, Botvinick Elliot L

机构信息

Department of Bioengineering, University of California at San Diego, La Jolla, California 92093-0435, USA.

出版信息

Microsc Res Tech. 2006 Nov;69(11):894-902. doi: 10.1002/jemt.20359.

DOI:10.1002/jemt.20359
PMID:16892192
Abstract

We have developed a microscope system for real-time single sperm tracking with an automated laser tweezers escape power assay. Phase contrast images of swimming sperm are digitized to the computer at video rate. The custom algorithm creates a region of interest centered about a sperm in response to a mouse click and performs all subsequent tasks autonomously. Microscope stage movement responds to feedback from video analysis of swimming sperm to center the sperm with respect to the field of view. For escape power assays, sperm are automatically relocated to the laser trap focus where they are held for a user-defined duration at fixed power, or held as laser power is gradually reduced. The sperm's position is automatically monitored to measure the laser power at which the sperm escapes the trap. Sperm are tracked for extended durations before and after laser trap experiments. Motility measurements including the curvilinear velocity and the absolute position of the sperm relative to the cell chamber are calculated and written to the hard drive at video rate. Experimental throughput is increased over 30 times compared to off-line data analysis. The efficacy of the "track and trap" algorithm is validated through examples and comparisons with the manually collected data.

摘要

我们开发了一种用于实时单精子追踪的显微镜系统,并带有自动激光镊子逃逸功率测定法。游动精子的相差图像以视频速率数字化传输到计算机中。自定义算法会根据鼠标点击操作创建一个以精子为中心的感兴趣区域,并自动执行所有后续任务。显微镜载物台的移动会响应游动精子视频分析的反馈,以使精子相对于视野居中。对于逃逸功率测定,精子会自动重新定位到激光阱焦点处,在该焦点处以固定功率将其捕获一段用户定义的持续时间,或者在激光功率逐渐降低时保持捕获状态。会自动监测精子的位置,以测量精子逃离陷阱时的激光功率。在激光阱实验之前和之后,对精子进行长时间追踪。计算包括曲线速度和精子相对于细胞室的绝对位置在内的运动性测量值,并以视频速率写入硬盘。与离线数据分析相比,实验通量提高了30倍以上。通过示例以及与手动收集数据的比较,验证了“追踪与捕获”算法的有效性。

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